code: def load_state_dict(self, state_dict, blocking=True):
      _buffer = io.BytesIO()
      torch.save(state_dict, _buffer)
      state_stream = _buffer.getvalue()
      state_id = ray.put(state_stream)
  
      remote_calls = [
          worker.load_state_stream.remote(state_id)
          for worker in self.remote_workers
      ]
      if blocking:
          ray.get(remote_calls)
  
imports: import io
  import types
  import logging
  import numbers
  import torch
  import numpy
  from zoo.orca.data.ray_xshards import RayXShards
  from zoo.orca.learn.pytorch.training_operator import TrainingOperator
  from zoo.orca.learn.pytorch.torch_runner import TorchRunner
  from zoo.orca.learn.utils import maybe_dataframe_to_xshards, dataframe_to_xshards, convert_predict_xshards_to_dataframe, update_predict_xshards, process_xshards_of_pandas_dataframe
  from zoo.ray import RayContext
  import ray
  from ray.exceptions import RayActorError
label_desc: Use image node_id 12 for calling the PyTorchRayEstimator obj's underlying member method code with example usage: obj.load_state_dict(state_dict, blocking) without return types